A Survey of Personalized Large Language Models: Progress and Future Directions

TL;DR

This survey reviews recent advancements in Personalized Large Language Models (PLLMs), highlighting techniques at input, model, and objective levels, and discusses current limitations and future research directions to enhance user-specific personalization.

Contribution

It provides a comprehensive overview of technical approaches for PLLMs, including prompting, finetuning, and alignment, along with insights into challenges and future research avenues.

Findings

Summarizes recent progress in PLLMs across three technical perspectives.

Identifies key limitations in current personalization methods.

Suggests promising future research directions for PLLMs.

Abstract

Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences. Personalized Large Language Models (PLLMs) tackle these challenges by leveraging individual user data, such as user profiles, historical dialogues, content, and interactions, to deliver responses that are contextually relevant and tailored to each user's specific needs. This is a highly valuable research topic, as PLLMs can significantly enhance user satisfaction and have broad applications in conversational agents, recommendation systems, emotion recognition, medical assistants, and more. This survey reviews recent advancements in PLLMs from three technical perspectives: prompting for personalized context (input level), finetuning for personalized adapters (model level), and alignment for personalized preferences (objective level). To provide deeper insights, we also discuss current limitations and outline several promising directions for future research. Updated information about this survey can be found at the https://github.com/JiahongLiu21/Awesome-Personalized-Large-Language-Models.